Project Members:
Prof. Dr. Verena Keitel
verena.keitel@med.uni-duesseldorf.de
Universitätsklinikum Düsseldorf
Dr. Maria Reich
maria.reich@uni-duesseldorf.de
Universitätsklinikum Düsseldorf
Prof. Dr. Mathias Heikenwälder
m.heikenwaelder@dkfz-heidelberg.de
DKFZ Heidelberg
QBiC contacts:
Dr. Gisela Gabernet
Bioinformatics project manager
gisela.gabernet@qbic.uni-tuebingen.de
Loading all the individual samples.
Merging all samples in the dataset. The table represents the number of cells that are available for each sample.
| Condition | Cell_number |
|---|---|
| 216-01 | 5300 |
| 216-02 | 4919 |
| 216-03 | 7252 |
| 216-04 | 6426 |
| 216-05 | 9975 |
| 216-09 | 7520 |
| 216-10 | 3498 |
| 216-11 | 9557 |
Adding sample conditions. The table represents the number of cells that are available for each condition.
| Condition | Cell_number |
|---|---|
| MDR2_KO | 28953 |
| TGR5_high | 25494 |
Important QC params for eliminating bad quality cells (could be droplets without cells) are:
Calculating the percentage of genes mapping to mitochondrial genome for QC:
Visualization of the QC metrics:
Low quality cells need to be filtered out:
After filtering low quality cells, the cell numbers per sample are the following:
| Condition | Cell_number |
|---|---|
| 216-01 | 4276 |
| 216-02 | 4442 |
| 216-03 | 6359 |
| 216-04 | 5768 |
| 216-05 | 8795 |
| 216-09 | 6861 |
| 216-10 | 2876 |
| 216-11 | 8387 |
Violin plot after filtering
By default, we employ a global-scaling normalization method "LogNormalize" that normalizes the feature expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result.
Detection of highly variable features. The displayed gene names represent the top 10 most variable genes.
## When using repel, set xnudge and ynudge to 0 for optimal results
Applies PCA to the highly variable features, after a standard scaling of the features to a mean of 0 and SD of 1.
## Centering and scaling data matrix
## PC_ 1
## Positive: Psap, Cst3, Ctsb, Ctsd, Ifi27l2a, Crip1, Zeb2, Lgmn, Fcer1g, Axl
## Hexa, Ctss, Cd74, Pltp, Laptm5, Klf2, Tyrobp, Csf1r, Rgs2, Gngt2
## C1qc, Lyz2, C1qa, Srgn, Ifi203, Aif1, C1qb, Cotl1, Mndal, Lst1
## Negative: Aldob, Fbp1, Chchd10, Ttc36, Stard10, Gsta3, Serpina1a, Bhmt, Ttr, Apoc4
## Cdo1, Rida, Apoc3, Urah, Gnmt, Serpina1b, Rbp4, Serpina1c, Hpd, Apoa2
## Ass1, Tdo2, Fabp1, Otc, Serpina1d, Uox, Phyh, Orm1, Car3, Angptl3
## PC_ 2
## Positive: Ctss, C1qc, Tyrobp, C1qa, C1qb, Laptm5, Aif1, Wfdc17, Mpeg1, Cybb
## Lyz2, Ctsc, Csf1r, Fcer1g, Lst1, Cd300c2, Lcp1, Adgre1, Ccl6, Fyb
## Cd5l, Fcgr3, Ly86, Clec4f, Ptprc, Vsig4, Rgs1, Cd52, Cd68, Cfp
## Negative: Sparc, Igfbp7, Serpinh1, Bgn, Col3a1, Col1a2, Ccdc80, Col6a1, Col1a1, Dcn
## Col14a1, Col6a2, Cygb, Pcolce, Fstl1, Aebp1, Lum, Loxl1, Htra1, Adamts2
## Ltbp4, Col6a3, Dpt, Prelp, Col5a2, Lhfp, Fbln1, Serping1, Abi3bp, Gas6
## PC_ 3
## Positive: Epcam, Cd24a, Sorbs2, Krt8, Sox9, Tspan8, Plet1, Krt19, Ehf, Sftpd
## Mfge8, Chka, Fxyd3, Wfdc2, Ddit4l, Krt23, Tinagl1, Tnfrsf12a, Ccn1, Rgs5
## Muc1, Plscr1, Atf3, Ankrd1, Hbegf, Krt7, Kcne3, Klf5, Tuft1, Clcf1
## Negative: Col1a2, Col3a1, Col1a1, Lum, Dcn, Dpt, Col6a1, Cygb, Mmp2, Col6a2
## Fbln1, Cfh, Mfap4, Islr, Adamts2, Colec12, Loxl1, Col14a1, Htra3, Ptgis
## Lrp1, Ms4a4d, Rarres2, Cxcl12, Serpinf1, Emp3, Abi3bp, Pdgfra, Gsn, Col5a1
## PC_ 4
## Positive: Epcam, C3, Krt8, Sox9, Efemp1, Cd24a, Krt19, Plet1, Tspan8, Fn1
## Sod3, Tmem45a, Itih5, Fxyd3, Ehf, Sftpd, Tnfrsf12a, Wnt4, Wfdc2, Chka
## Atf3, Gas6, Krt23, Ddit4l, Sorbs2, Krt7, Rgs5, Gadd45b, Muc1, Ankrd1
## Negative: Ptprb, Adgrf5, Fabp4, Aqp1, Pecam1, Adgrl4, Kdr, Tek, Mmrn2, Cyyr1
## Clec14a, Flt1, F8, Jam2, Calcrl, Ehd3, Egfl7, Gpihbp1, Esam, Eng
## Ramp2, Plvap, Emcn, Myct1, Tie1, Pde2a, Gpr182, Clec4g, Cemip2, Vwf
## PC_ 5
## Positive: Sparcl1, Des, Myl9, Lmod1, Gm13889, Itga8, Mylk, Atp1a2, Mustn1, Pi15
## Tagln, Gucy1b1, Mamdc2, Tbx2, Emilin1, Prrx1, Abcc9, Pde1a, Mfap4, Hand2os1
## Ebf1, Myh11, Cxcl12, Lum, Pdgfrb, Rgs7bp, Crispld2, Gem, Olfml3, Lbh
## Negative: Upk3b, Upk1b, Msln, Lrrn4, Cldn15, Rspo1, Nkain4, Igfbp6, Spock2, Muc16
## Bst1, Bnc1, Sulf1, Lrp2, Pkhd1l1, Slc16a1, Gpc3, Atp6v0a4, Igfbp5, Tmem151a
## Stk26, Myl7, Adgrd1, Chst4, Cybrd1, Clic3, Cyp2s1, Slc39a8, Prss12, Enpp6
## Saving 7 x 5 in image
## Saving 7 x 5 in image
Determining number of PCAs to consider for clustering from Elbow plot. It is recommended to go rather on the higher end of PCAs.
## Saving 7 x 5 in image
## Saving 7 x 5 in image
As in PhenoGraph, we first construct a KNN graph based on the euclidean distance in PCA space, and refine the edge weights between any two cells based on the shared overlap in their local neighborhoods (Jaccard similarity).
This step is performed using the FindNeighbors function, and takes as input the previously defined dimensionality of the dataset.
To cluster the cells, we next apply modularity optimization techniques such as the Louvain algorithm (default) or SLM, to iteratively group cells together, with the goal of optimizing the standard modularity function.
## Computing nearest neighbor graph
## Computing SNN
Non-linear dimensionality reduction method UMAP was applied to visualize the cell clusters.
## 22:09:01 UMAP embedding parameters a = 0.9922 b = 1.112
## 22:09:01 Read 47764 rows and found 15 numeric columns
## 22:09:01 Using Annoy for neighbor search, n_neighbors = 30
## 22:09:01 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 22:09:09 Writing NN index file to temp file /var/folders/2z/rqg0lktx6nq0_wqgs1yrx0lw0000gn/T//Rtmpcslb0s/file1487437202936
## 22:09:09 Searching Annoy index using 1 thread, search_k = 3000
## 22:09:28 Annoy recall = 100%
## 22:09:29 Commencing smooth kNN distance calibration using 1 thread
## 22:09:31 Initializing from normalized Laplacian + noise
## 22:09:44 Commencing optimization for 200 epochs, with 2008542 positive edges
## 22:10:11 Optimization finished
## Saving 7 x 5 in image
## Saving 7 x 5 in image
Finding markers (genes) that define clusters via differential gene expression expression. A table containing the top markers found for each of the clusters can be found under results/markers/.
## Calculating cluster 0
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
## Calculating cluster 6
## Calculating cluster 7
## Calculating cluster 8
## Calculating cluster 9
## Calculating cluster 10
## Calculating cluster 11
## Calculating cluster 12
## Calculating cluster 13
## Calculating cluster 14
## Calculating cluster 15
## Calculating cluster 16
## Calculating cluster 17
Expression heatmap for the top 20 markers for each cluster. All plots for the markers can be found under results/markers.
All marker plots in this section can be found under results/markers.
Choose the cell type:
Provided Hepatic stellate cells markers: Des, Reln. Other markers in this cluster: Dcn, Gsn, Cxcl12, Lum
## Warning in FetchData(object = object, vars = c(dims, "ident", features), : The
## following requested variables were not found: Serpina 1b
Plotting provided liver progenitor cells (LPC) markers.
UMAP plot with cell type assignments according to gene expression profiles of the clusters. All UMAP plots are found under results/umap.
## Saving 7 x 5 in image
## Saving 7 x 5 in image
Number of cells identified for each of the cell types:
| Condition | Cell_number |
|---|---|
| Cholangiocytes | 16060 |
| Macrophage / Kupffer | 10284 |
| Hepatocytes | 12306 |
| HSC | 2880 |
| Endothelial | 3426 |
| T-lymphocytes | 844 |
| Endothelial vascular | 651 |
| Fibroblasts | 590 |
| B-lymphocytes | 434 |
| Lymphocytes | 289 |
Heatmap with new cluster labels:
The differential gene expression analysis was performed for each of the cell types, by comparing the MDR2 KO to the WT mice population. Here the results tables are displayed for each cell population.
All tables for the Differential Gene Expression analysis can be found under results/DE_genes.
| p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | gene | log_pval_adj | |
|---|---|---|---|---|---|---|---|
| Pnkd | 0 | 0.6354260 | 0.882 | 0.650 | 0 | Pnkd | Inf |
| Tmbim1 | 0 | 0.5556900 | 0.657 | 0.323 | 0 | Tmbim1 | Inf |
| Aamp | 0 | 0.4378888 | 0.818 | 0.650 | 0 | Aamp | Inf |
| Ddx3y | 0 | 0.3382718 | 0.157 | 0.000 | 0 | Ddx3y | Inf |
| Hspa8 | 0 | -0.3110741 | 0.964 | 0.989 | 0 | Hspa8 | Inf |
| Xist | 0 | -0.4108656 | 0.649 | 0.985 | 0 | Xist | Inf |
| Serpina10 | 0 | -0.8454248 | 0.195 | 0.553 | 0 | Serpina10 | Inf |
| Serpina6 | 0 | -1.5215662 | 0.048 | 0.385 | 0 | Serpina6 | Inf |
| mt-Nd5 | 0 | 0.3964567 | 0.964 | 0.921 | 0 | mt-Nd5 | 265.8403 |
| Pigr | 0 | 0.7072268 | 0.604 | 0.396 | 0 | Pigr | 236.5766 |
| Krt8 | 0 | -0.3640415 | 0.986 | 0.995 | 0 | Krt8 | 220.9421 |
| Fabp1 | 0 | 0.4103162 | 0.495 | 0.272 | 0 | Fabp1 | 180.6191 |
| Igfbp4 | 0 | -0.3751494 | 0.368 | 0.581 | 0 | Igfbp4 | 174.5309 |
| Malat1 | 0 | 0.2795960 | 0.999 | 0.998 | 0 | Malat1 | 154.8080 |
| Kcne3 | 0 | 0.4217557 | 0.674 | 0.506 | 0 | Kcne3 | 152.9720 |
| Gm42418 | 0 | 0.3588910 | 0.979 | 0.954 | 0 | Gm42418 | 152.1815 |
| Dynll1 | 0 | -0.2758041 | 0.698 | 0.835 | 0 | Dynll1 | 142.9373 |
| Sparc | 0 | -0.2862455 | 0.822 | 0.907 | 0 | Sparc | 139.2745 |
| Actg1 | 0 | -0.2897031 | 0.991 | 0.995 | 0 | Actg1 | 136.4482 |
| Txnip | 0 | 0.3510106 | 0.833 | 0.736 | 0 | Txnip | 136.1171 |
## When using repel, set xnudge and ynudge to 0 for optimal results
| p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | gene | log_pval_adj | |
|---|---|---|---|---|---|---|---|
| Tmbim1 | 0 | 0.6672841 | 0.725 | 0.389 | 0 | Tmbim1 | 105.52471 |
| Pnkd | 0 | 0.4730268 | 0.787 | 0.499 | 0 | Pnkd | 79.57374 |
| Aamp | 0 | 0.4249373 | 0.776 | 0.533 | 0 | Aamp | 67.99756 |
| Ddx3y | 0 | 0.2949511 | 0.114 | 0.000 | 0 | Ddx3y | 40.00707 |
| B2m | 0 | 0.4272555 | 0.941 | 0.899 | 0 | B2m | 32.55355 |
| mt-Nd4l | 0 | 0.3163706 | 0.915 | 0.812 | 0 | mt-Nd4l | 32.49410 |
| H2-D1 | 0 | 0.5592761 | 0.912 | 0.846 | 0 | H2-D1 | 30.46005 |
| Ubc | 0 | -0.3088392 | 0.899 | 0.951 | 0 | Ubc | 30.02219 |
| Spp1 | 0 | -0.8331191 | 0.455 | 0.597 | 0 | Spp1 | 26.33048 |
| mt-Nd5 | 0 | 0.2755401 | 0.912 | 0.826 | 0 | mt-Nd5 | 24.61970 |
| Anxa1 | 0 | 0.3235805 | 0.768 | 0.614 | 0 | Anxa1 | 21.38562 |
| Rps2 | 0 | -0.3303674 | 0.507 | 0.652 | 0 | Rps2 | 18.55778 |
| Efemp1 | 0 | 0.2982785 | 0.573 | 0.397 | 0 | Efemp1 | 17.56830 |
| Sparc | 0 | -0.2832436 | 0.993 | 0.988 | 0 | Sparc | 17.28275 |
| mt-Nd3 | 0 | -0.2531829 | 0.867 | 0.871 | 0 | mt-Nd3 | 15.09504 |
| Gstm1 | 0 | 0.2682395 | 0.936 | 0.883 | 0 | Gstm1 | 13.92353 |
| Penk | 0 | 0.3607909 | 0.274 | 0.146 | 0 | Penk | 13.65636 |
| Jund | 0 | 0.3125016 | 0.957 | 0.933 | 0 | Jund | 13.31948 |
| Ogn | 0 | 0.3431669 | 0.677 | 0.551 | 0 | Ogn | 11.36462 |
| H2-K1 | 0 | 0.3373740 | 0.898 | 0.863 | 0 | H2-K1 | 10.85760 |
## When using repel, set xnudge and ynudge to 0 for optimal results
| p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | gene | log_pval_adj | |
|---|---|---|---|---|---|---|---|
| Mpp6 | 0e+00 | -0.7901515 | 0.168 | 0.590 | 0.0000000 | Mpp6 | 24.993789 |
| Tmbim1 | 0e+00 | 0.5207181 | 0.704 | 0.369 | 0.0000000 | Tmbim1 | 15.260526 |
| B2m | 0e+00 | 0.5200070 | 0.980 | 0.962 | 0.0000000 | B2m | 15.094202 |
| Aamp | 0e+00 | 0.4475090 | 0.808 | 0.570 | 0.0000000 | Aamp | 13.777739 |
| Pnkd | 0e+00 | 0.5055122 | 0.751 | 0.498 | 0.0000000 | Pnkd | 13.637931 |
| Sparc | 0e+00 | -0.4148776 | 0.993 | 0.993 | 0.0000000 | Sparc | 9.885798 |
| H2-D1 | 0e+00 | 0.5990800 | 0.953 | 0.959 | 0.0000000 | H2-D1 | 7.615496 |
| Ang | 0e+00 | 0.3831581 | 0.488 | 0.218 | 0.0000001 | Ang | 7.202999 |
| Ddx3y | 0e+00 | 0.2747410 | 0.155 | 0.000 | 0.0000001 | Ddx3y | 7.087120 |
| Xist | 0e+00 | -0.4704138 | 0.461 | 0.737 | 0.0000005 | Xist | 6.295809 |
| Ubc | 0e+00 | -0.3773012 | 0.916 | 0.952 | 0.0000009 | Ubc | 6.029792 |
| Rarres2 | 0e+00 | 0.3793868 | 0.990 | 1.000 | 0.0000011 | Rarres2 | 5.978549 |
| H2-K1 | 0e+00 | 0.3990797 | 0.963 | 0.983 | 0.0000047 | H2-K1 | 5.327477 |
| Rnase4 | 0e+00 | 0.2731783 | 0.970 | 0.973 | 0.0000171 | Rnase4 | 4.767019 |
| Rps2 | 0e+00 | -0.3525257 | 0.552 | 0.751 | 0.0000400 | Rps2 | 4.397639 |
| Serpinh1 | 0e+00 | -0.3024662 | 0.902 | 0.962 | 0.0001277 | Serpinh1 | 3.893836 |
| 2010001K21Rik | 0e+00 | 0.2782936 | 0.448 | 0.218 | 0.0001511 | 2010001K21Rik | 3.820779 |
| mt-Nd5 | 0e+00 | 0.3242000 | 0.936 | 0.870 | 0.0003312 | mt-Nd5 | 3.479887 |
| Spp1 | 0e+00 | -0.5913436 | 0.414 | 0.577 | 0.0003561 | Spp1 | 3.448455 |
| Serpinb6b | 1e-07 | -0.4237194 | 0.758 | 0.843 | 0.0019910 | Serpinb6b | 2.700937 |
## When using repel, set xnudge and ynudge to 0 for optimal results
| p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | gene | log_pval_adj | |
|---|---|---|---|---|---|---|---|
| Aamp | 0 | 0.6254687 | 0.744 | 0.477 | 0 | Aamp | Inf |
| Fau | 0 | -0.3061068 | 0.964 | 0.990 | 0 | Fau | 273.3556 |
| Rps15a | 0 | -0.3144404 | 0.943 | 0.973 | 0 | Rps15a | 244.2381 |
| Fabp1 | 0 | 1.6037711 | 0.536 | 0.237 | 0 | Fabp1 | 241.4718 |
| Rps11 | 0 | -0.3361929 | 0.929 | 0.967 | 0 | Rps11 | 231.9247 |
| Rpl32 | 0 | -0.3249804 | 0.924 | 0.969 | 0 | Rpl32 | 230.4496 |
| Rpl27a | 0 | -0.2827200 | 0.944 | 0.972 | 0 | Rpl27a | 214.2065 |
| Rpl34 | 0 | -0.2959929 | 0.931 | 0.971 | 0 | Rpl34 | 213.6860 |
| Rpl39 | 0 | -0.2777258 | 0.953 | 0.977 | 0 | Rpl39 | 204.5159 |
| Rpl37 | 0 | -0.2659561 | 0.953 | 0.975 | 0 | Rpl37 | 171.0503 |
| Cd83 | 0 | -0.5731607 | 0.524 | 0.730 | 0 | Cd83 | 168.2408 |
| Slc11a1 | 0 | 0.5110704 | 0.748 | 0.677 | 0 | Slc11a1 | 167.9841 |
| Rpl18a | 0 | -0.2811977 | 0.921 | 0.969 | 0 | Rpl18a | 167.2244 |
| Rps5 | 0 | -0.2596402 | 0.924 | 0.969 | 0 | Rps5 | 167.1390 |
| Cd52 | 0 | -0.3801953 | 0.839 | 0.938 | 0 | Cd52 | 165.1401 |
| Pnkd | 0 | 0.4372257 | 0.544 | 0.322 | 0 | Pnkd | 157.7877 |
| Dusp2 | 0 | -0.5773107 | 0.277 | 0.515 | 0 | Dusp2 | 148.1814 |
| Rpl6 | 0 | -0.2611276 | 0.924 | 0.964 | 0 | Rpl6 | 144.8840 |
| Rpl28 | 0 | -0.2722368 | 0.901 | 0.963 | 0 | Rpl28 | 142.5081 |
| Rpl36 | 0 | -0.2623005 | 0.890 | 0.951 | 0 | Rpl36 | 138.5803 |
## When using repel, set xnudge and ynudge to 0 for optimal results
| p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | gene | log_pval_adj | |
|---|---|---|---|---|---|---|---|
| Tmbim1 | 0 | 0.7909171 | 0.740 | 0.363 | 0 | Tmbim1 | 139.70856 |
| Pnkd | 0 | 0.5728983 | 0.719 | 0.407 | 0 | Pnkd | 90.86566 |
| H2-D1 | 0 | 0.5341454 | 0.966 | 0.945 | 0 | H2-D1 | 89.95554 |
| H2-K1 | 0 | 0.4955551 | 0.965 | 0.932 | 0 | H2-K1 | 85.10493 |
| B2m | 0 | 0.4529357 | 0.965 | 0.924 | 0 | B2m | 79.93364 |
| Aamp | 0 | 0.5422947 | 0.656 | 0.385 | 0 | Aamp | 73.10954 |
| Rpl37a | 0 | -0.2750290 | 0.979 | 0.986 | 0 | Rpl37a | 60.55155 |
| Rps29 | 0 | -0.2561387 | 0.951 | 0.965 | 0 | Rps29 | 39.57848 |
| H2-Q6 | 0 | 0.3634977 | 0.416 | 0.205 | 0 | H2-Q6 | 37.81407 |
| H2-T23 | 0 | 0.3681642 | 0.828 | 0.687 | 0 | H2-T23 | 34.21035 |
| H2-Q7 | 0 | 0.3833302 | 0.499 | 0.302 | 0 | H2-Q7 | 30.13458 |
| Rps18 | 0 | -0.3647375 | 0.456 | 0.653 | 0 | Rps18 | 29.47065 |
| Rps2 | 0 | -0.3543401 | 0.444 | 0.623 | 0 | Rps2 | 24.63197 |
| Klf2 | 0 | 0.3584539 | 0.918 | 0.874 | 0 | Klf2 | 24.29165 |
| Ftl1 | 0 | 0.2592899 | 0.982 | 0.961 | 0 | Ftl1 | 23.25587 |
| mt-Nd4l | 0 | 0.2743161 | 0.811 | 0.678 | 0 | mt-Nd4l | 19.27798 |
| mt-Nd5 | 0 | 0.2634792 | 0.791 | 0.660 | 0 | mt-Nd5 | 19.16932 |
| Fxyd6 | 0 | 0.3093772 | 0.300 | 0.155 | 0 | Fxyd6 | 18.58784 |
| Sparc | 0 | -0.3100792 | 0.819 | 0.879 | 0 | Sparc | 18.54379 |
| Xist | 0 | -0.2865103 | 0.729 | 0.855 | 0 | Xist | 18.05250 |
## When using repel, set xnudge and ynudge to 0 for optimal results
| p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | gene | log_pval_adj | |
|---|---|---|---|---|---|---|---|
| Aamp | 0.0e+00 | 0.6124211 | 0.671 | 0.338 | 0.0000000 | Aamp | 15.5881326 |
| Tmbim1 | 0.0e+00 | 0.6063314 | 0.621 | 0.257 | 0.0000000 | Tmbim1 | 15.4778075 |
| Pnkd | 0.0e+00 | 0.5826073 | 0.706 | 0.421 | 0.0000000 | Pnkd | 13.5542901 |
| Fabp1 | 0.0e+00 | 1.1575273 | 0.476 | 0.193 | 0.0000000 | Fabp1 | 9.1618052 |
| H2-D1 | 0.0e+00 | 0.3975316 | 0.965 | 0.971 | 0.0000003 | H2-D1 | 6.4956070 |
| Rpl37a | 0.0e+00 | -0.2507606 | 0.971 | 0.981 | 0.0000022 | Rpl37a | 5.6666301 |
| Sparc | 0.0e+00 | -0.6113349 | 0.553 | 0.717 | 0.0000037 | Sparc | 5.4370717 |
| Xist | 0.0e+00 | -0.3369773 | 0.803 | 0.932 | 0.0000050 | Xist | 5.3040000 |
| Col4a2 | 0.0e+00 | -0.4118840 | 0.482 | 0.666 | 0.0000174 | Col4a2 | 4.7585431 |
| Rps2 | 0.0e+00 | -0.3977003 | 0.412 | 0.621 | 0.0002008 | Rps2 | 3.6971962 |
| Gstm1 | 0.0e+00 | 0.4673732 | 0.559 | 0.357 | 0.0006989 | Gstm1 | 3.1556036 |
| Fos | 0.0e+00 | 0.4666698 | 0.865 | 0.733 | 0.0008460 | Fos | 3.0726062 |
| Fxyd1 | 1.0e-07 | 0.3353177 | 0.524 | 0.309 | 0.0020677 | Fxyd1 | 2.6845114 |
| mt-Nd3 | 4.0e-07 | -0.3843444 | 0.547 | 0.685 | 0.0135784 | mt-Nd3 | 1.8671512 |
| Nrp2 | 4.0e-07 | -0.3962025 | 0.874 | 0.894 | 0.0137220 | Nrp2 | 1.8625831 |
| Stxbp6 | 1.0e-06 | 0.3224920 | 0.476 | 0.293 | 0.0331104 | Stxbp6 | 1.4800360 |
| H2-K1 | 1.4e-06 | 0.2763872 | 0.971 | 0.949 | 0.0449407 | H2-K1 | 1.3473601 |
| Stab2 | 1.5e-06 | -0.4697030 | 0.291 | 0.453 | 0.0497519 | Stab2 | 1.3031903 |
| Gm38832 | 1.9e-06 | -0.3548581 | 0.044 | 0.151 | 0.0607635 | Gm38832 | 1.2163575 |
| Nsg1 | 4.9e-06 | 0.3126703 | 0.544 | 0.360 | 0.1575257 | Nsg1 | 0.8026487 |
## When using repel, set xnudge and ynudge to 0 for optimal results
| p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | gene | log_pval_adj | |
|---|---|---|---|---|---|---|---|
| Scd1 | 0 | 0.9245891 | 0.759 | 0.486 | 0 | Scd1 | Inf |
| Aox3 | 0 | 0.8987515 | 0.831 | 0.611 | 0 | Aox3 | Inf |
| Pnkd | 0 | 0.5593918 | 0.836 | 0.663 | 0 | Pnkd | Inf |
| Aamp | 0 | 0.5216661 | 0.801 | 0.578 | 0 | Aamp | Inf |
| Fabp1 | 0 | 0.4597307 | 0.998 | 0.985 | 0 | Fabp1 | Inf |
| Ftl1 | 0 | 0.4000707 | 1.000 | 0.997 | 0 | Ftl1 | Inf |
| Serpina12 | 0 | 1.0664946 | 0.626 | 0.354 | 0 | Serpina12 | 288.4392 |
| Mup21 | 0 | 1.3750889 | 0.355 | 0.073 | 0 | Mup21 | 256.2281 |
| Car3 | 0 | 0.5929682 | 0.987 | 0.964 | 0 | Car3 | 222.9452 |
| Ubc | 0 | -0.4238153 | 0.888 | 0.941 | 0 | Ubc | 193.3145 |
| Elovl3 | 0 | 0.4935397 | 0.222 | 0.012 | 0 | Elovl3 | 191.1047 |
| Glud1 | 0 | 0.3261374 | 0.971 | 0.952 | 0 | Glud1 | 177.1571 |
| Insig2 | 0 | -0.4151271 | 0.809 | 0.894 | 0 | Insig2 | 173.4919 |
| Akr1c19 | 0 | -0.3618512 | 0.350 | 0.578 | 0 | Akr1c19 | 167.1456 |
| Nnmt | 0 | -0.4757936 | 0.756 | 0.838 | 0 | Nnmt | 166.8207 |
| Hsd3b5 | 0 | 0.2951100 | 0.178 | 0.013 | 0 | Hsd3b5 | 141.3752 |
| Hpd | 0 | -0.2732340 | 0.983 | 0.967 | 0 | Hpd | 135.5089 |
| Gm31583 | 0 | 0.4116757 | 0.356 | 0.147 | 0 | Gm31583 | 135.1967 |
| Rps2 | 0 | 0.4116527 | 0.761 | 0.668 | 0 | Rps2 | 126.2926 |
| 1810008I18Rik | 0 | 0.4620801 | 0.655 | 0.481 | 0 | 1810008I18Rik | 125.1804 |
## When using repel, set xnudge and ynudge to 0 for optimal results
Saving R Seurat object:
For the single-cell data analysis the R package Seurat v3.2.2 was employed. Graphs were produced in RStudio with R version 4.0.3 (2020-10-10) mainly using the R package ggplot2 v3.3.2. Final reports were produced using the R package rmarkdown v2.6, with knitr v1.30.